Miaotian Tang,1 Meng Zhang,1 Yu Dang,1 Mingxing Lei,2 Dianying Zhang1,3– 5 

1Department of Trauma Orthopaedics, Peking University People’s Hospital, Beijing, 100044, People’s Republic of China; 2Department of Orthopaedics, Hainan Hospital of Chinese PLA General Hospital, Sanya, 572013, People’s Republic of China; 3National Trauma Medical Center, Beijing, 100044, People’s Republic of China; 4Key Laboratory of Trauma Treatment and Neural Regeneration, Ministry of Education, Beijing, 100044, People’s Republic of China; 5Department of Orthopedics, Peking University Binhai Hospital, Tianjin, 300450, People’s Republic of China

Correspondence: Dianying Zhang, Department of Trauma Orthopaedics, Peking University People’s Hospital, Beijing, 100044, People’s Republic of China, Tel +86 010-88326550, Email zdy8016@163.com Mingxing Lei, Department of Orthopaedics, Hainan Hospital of Chinese PLA General Hospital, Sanya, 572013, People’s Republic of China, Tel +8618811772189, Email leimingxing2@sina.com

Background: Hip fractures have become a significant health concern, particularly among super-aged patients, who were at a high risk of postoperative pneumonia due to their frailty and the presence of multiple comorbidities. This study aims to establish and validate a model to predict postoperative pneumonia among super-aged patients with hip fracture.
Methods: Data were derived from the Chinese PLA General Hospital (PLAGH) Hip Fracture Cohort Study, and we included 555 super-aged patients (≧80 years old) with hip fracture treated with surgery. Patient’s demographics, comorbidities, laboratory tests, and surgery types were collected for analysis. All patients were randomly splitting into a training group and a validation group according to the ratio of 7:3. The majority of patients were used to train models, which was tuned using a series of algorithms, including decision tree (DT), random forest (RF), extreme gradient boosting machine (eXGBM), support vector machine (SVM), neural network (NN), and logistic regression (LR).
Results: The incidence of postoperative pneumonia was 7.2% (40/555). Among the six developed models, the eXGBM model demonstrated the optimal model, with the area under the curve (AUC) value of 0.929 (95% CI: 0.900– 0.959), followed by the RF model (AUC: 0.916, 95% CI: 0.885– 0.948). The LR model had an AUC value of 0.720 (95% CI: 0.662– 0.778). In addition, the eXGBM model demonstrated the optimal prediction performance in terms of accuracy (0.858), precision (0.870), F1 score (0.855), Brier score (0.104), and log loss (0.349). It also showed favorable calibration ability and favorable clinical net benefits across various threshold risk.
Conclusion: This study develops and validates a reliable machine learning-based model to predict pneumonia specifically among super-aged patients with hip fracture following surgery. This model can serve as a useful tool to identify postoperative pneumonia and guide clinical strategies for super-aged patients with hip fracture.

Keywords: machine learning, postoperative pneumonia, hip fracture, super-aged patients, geriatric patients